Yang et al. [1]} proposed a deep de-aliasing generative adversarial network (DAGAN) that uses a residual architecture as the network generator responsible for reconstructing the images associated to a loss function that has four components: an image domain loss, a frequency domain loss, a perceptual loss, and an adversarial loss. Quan et al. [2]} proposed a generative adversarial network (GAN) with a cyclic loss [3]}. Their method consists of a cascade of a reconstruction network followed by a refinement network with a cyclic loss component that tries to enforce that the model is bijective. Schlemper et al. [4]} proposed a deep cascade of convolutional neural networks (CNNs) that has data consistency (DC) blocks between consecutive subnetworks in the cascade. Their hypothesis is that the DC blocks reduce the issue of overfitting, therefore allowing the training of deeper models.
